Overview

Dataset statistics

Number of variables14
Number of observations47
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.3 KiB
Average record size in memory116.0 B

Variable types

Numeric13
Categorical1

Alerts

kmeans_labels has constant value ""Constant
Ash is highly overall correlated with Ash_AlcanityHigh correlation
Ash_Alcanity is highly overall correlated with AshHigh correlation
Color_Intensity is highly overall correlated with Hue and 1 other fieldsHigh correlation
Flavanoids is highly overall correlated with Nonflavanoid_Phenols and 3 other fieldsHigh correlation
Hue is highly overall correlated with Color_Intensity and 2 other fieldsHigh correlation
Nonflavanoid_Phenols is highly overall correlated with FlavanoidsHigh correlation
OD280 is highly overall correlated with Color_Intensity and 3 other fieldsHigh correlation
Proanthocyanins is highly overall correlated with Flavanoids and 1 other fieldsHigh correlation
Total_Phenols is highly overall correlated with Flavanoids and 3 other fieldsHigh correlation

Reproduction

Analysis started2023-11-28 13:22:32.365781
Analysis finished2023-11-28 13:23:01.662127
Duration29.3 seconds
Software versionydata-profiling vv4.6.2
Download configurationconfig.json

Variables

Alcohol
Real number (ℝ)

Distinct43
Distinct (%)91.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.052979
Minimum11.46
Maximum14.38
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size752.0 B
2023-11-28T13:23:01.781504image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum11.46
5-th percentile12.012
Q112.7
median13.05
Q313.495
95-th percentile14.186
Maximum14.38
Range2.92
Interquartile range (IQR)0.795

Descriptive statistics

Standard deviation0.66502996
Coefficient of variation (CV)0.050948521
Kurtosis-0.33143242
Mean13.052979
Median Absolute Deviation (MAD)0.44
Skewness-0.13478552
Sum613.49
Variance0.44226485
MonotonicityNot monotonic
2023-11-28T13:23:01.962857image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
12.93 2
 
4.3%
13.4 2
 
4.3%
12.7 2
 
4.3%
12.25 2
 
4.3%
13.64 1
 
2.1%
12.45 1
 
2.1%
13.32 1
 
2.1%
13.08 1
 
2.1%
13.5 1
 
2.1%
12.79 1
 
2.1%
Other values (33) 33
70.2%
ValueCountFrequency (%)
11.46 1
2.1%
11.87 1
2.1%
12 1
2.1%
12.04 1
2.1%
12.2 1
2.1%
12.25 2
4.3%
12.29 1
2.1%
12.36 1
2.1%
12.42 1
2.1%
12.45 1
2.1%
ValueCountFrequency (%)
14.38 1
2.1%
14.22 1
2.1%
14.21 1
2.1%
14.13 1
2.1%
13.88 1
2.1%
13.84 1
2.1%
13.73 1
2.1%
13.69 1
2.1%
13.64 1
2.1%
13.62 1
2.1%

Malic_Acid
Real number (ℝ)

Distinct43
Distinct (%)91.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.7289362
Minimum2.67
Maximum5.04
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size752.0 B
2023-11-28T13:23:02.164384image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum2.67
5-th percentile2.969
Q13.265
median3.74
Q34.07
95-th percentile4.687
Maximum5.04
Range2.37
Interquartile range (IQR)0.805

Descriptive statistics

Standard deviation0.57217067
Coefficient of variation (CV)0.15344073
Kurtosis-0.45435803
Mean3.7289362
Median Absolute Deviation (MAD)0.44
Skewness0.34844195
Sum175.26
Variance0.32737928
MonotonicityNot monotonic
2023-11-28T13:23:02.354951image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
3.43 2
 
4.3%
3.59 2
 
4.3%
3.17 2
 
4.3%
3.03 2
 
4.3%
3.26 1
 
2.1%
3.24 1
 
2.1%
3.9 1
 
2.1%
3.12 1
 
2.1%
2.67 1
 
2.1%
3.3 1
 
2.1%
Other values (33) 33
70.2%
ValueCountFrequency (%)
2.67 1
2.1%
2.81 1
2.1%
2.96 1
2.1%
2.99 1
2.1%
3.03 2
4.3%
3.1 1
2.1%
3.12 1
2.1%
3.17 2
4.3%
3.24 1
2.1%
3.26 1
2.1%
ValueCountFrequency (%)
5.04 1
2.1%
4.95 1
2.1%
4.72 1
2.1%
4.61 1
2.1%
4.6 1
2.1%
4.43 1
2.1%
4.36 1
2.1%
4.31 1
2.1%
4.3 1
2.1%
4.28 1
2.1%

Ash
Real number (ℝ)

HIGH CORRELATION 

Distinct35
Distinct (%)74.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4004255
Minimum1.82
Maximum2.86
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size752.0 B
2023-11-28T13:23:02.539483image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1.82
5-th percentile2.036
Q12.28
median2.38
Q32.55
95-th percentile2.727
Maximum2.86
Range1.04
Interquartile range (IQR)0.27

Descriptive statistics

Standard deviation0.21299069
Coefficient of variation (CV)0.088730387
Kurtosis0.30558081
Mean2.4004255
Median Absolute Deviation (MAD)0.13
Skewness-0.25175202
Sum112.82
Variance0.045365032
MonotonicityNot monotonic
2023-11-28T13:23:02.723293image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
2.38 4
 
8.5%
2.48 3
 
6.4%
2.54 2
 
4.3%
2.35 2
 
4.3%
2.36 2
 
4.3%
2.26 2
 
4.3%
2.28 2
 
4.3%
2.4 2
 
4.3%
2.32 2
 
4.3%
2.2 1
 
2.1%
Other values (25) 25
53.2%
ValueCountFrequency (%)
1.82 1
2.1%
1.98 1
2.1%
2 1
2.1%
2.12 1
2.1%
2.15 1
2.1%
2.19 1
2.1%
2.2 1
2.1%
2.21 1
2.1%
2.23 1
2.1%
2.26 2
4.3%
ValueCountFrequency (%)
2.86 1
2.1%
2.74 1
2.1%
2.73 1
2.1%
2.72 1
2.1%
2.7 1
2.1%
2.65 1
2.1%
2.64 1
2.1%
2.62 1
2.1%
2.61 1
2.1%
2.6 1
2.1%

Ash_Alcanity
Real number (ℝ)

HIGH CORRELATION 

Distinct23
Distinct (%)48.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.621277
Minimum13.2
Maximum27
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size752.0 B
2023-11-28T13:23:02.894997image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum13.2
5-th percentile16
Q118.95
median21
Q322.25
95-th percentile24.85
Maximum27
Range13.8
Interquartile range (IQR)3.3

Descriptive statistics

Standard deviation2.7740988
Coefficient of variation (CV)0.13452605
Kurtosis0.54846858
Mean20.621277
Median Absolute Deviation (MAD)2
Skewness-0.1135521
Sum969.2
Variance7.6956244
MonotonicityNot monotonic
2023-11-28T13:23:03.047975image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
20 5
 
10.6%
21 5
 
10.6%
19.5 4
 
8.5%
21.5 4
 
8.5%
22 3
 
6.4%
18.5 3
 
6.4%
23 3
 
6.4%
19 2
 
4.3%
16 2
 
4.3%
22.5 2
 
4.3%
Other values (13) 14
29.8%
ValueCountFrequency (%)
13.2 1
 
2.1%
15.2 1
 
2.1%
16 2
4.3%
17.5 1
 
2.1%
18 1
 
2.1%
18.5 3
6.4%
18.6 1
 
2.1%
18.8 1
 
2.1%
18.9 1
 
2.1%
19 2
4.3%
ValueCountFrequency (%)
27 1
 
2.1%
26.5 1
 
2.1%
25 1
 
2.1%
24.5 1
 
2.1%
24 2
4.3%
23.5 1
 
2.1%
23 3
6.4%
22.5 2
4.3%
22 3
6.4%
21.5 4
8.5%

Magnesium
Real number (ℝ)

Distinct25
Distinct (%)53.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean99.170213
Minimum80
Maximum128
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size752.0 B
2023-11-28T13:23:03.210921image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum80
5-th percentile80.6
Q188.5
median101
Q3106.5
95-th percentile118.8
Maximum128
Range48
Interquartile range (IQR)18

Descriptive statistics

Standard deviation11.801472
Coefficient of variation (CV)0.11900219
Kurtosis-0.46023796
Mean99.170213
Median Absolute Deviation (MAD)10
Skewness0.30352491
Sum4661
Variance139.27475
MonotonicityNot monotonic
2023-11-28T13:23:03.381166image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
88 5
 
10.6%
102 5
 
10.6%
80 3
 
6.4%
106 3
 
6.4%
112 3
 
6.4%
96 3
 
6.4%
101 2
 
4.3%
97 2
 
4.3%
107 2
 
4.3%
111 2
 
4.3%
Other values (15) 17
36.2%
ValueCountFrequency (%)
80 3
6.4%
82 1
 
2.1%
85 1
 
2.1%
86 1
 
2.1%
87 1
 
2.1%
88 5
10.6%
89 2
 
4.3%
90 1
 
2.1%
92 2
 
4.3%
96 3
6.4%
ValueCountFrequency (%)
128 1
 
2.1%
123 1
 
2.1%
120 1
 
2.1%
116 1
 
2.1%
113 1
 
2.1%
112 3
6.4%
111 2
4.3%
107 2
4.3%
106 3
6.4%
104 1
 
2.1%

Total_Phenols
Real number (ℝ)

HIGH CORRELATION 

Distinct38
Distinct (%)80.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9487234
Minimum0.98
Maximum3.25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size752.0 B
2023-11-28T13:23:03.578468image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.98
5-th percentile1.286
Q11.52
median1.8
Q32.31
95-th percentile2.958
Maximum3.25
Range2.27
Interquartile range (IQR)0.79

Descriptive statistics

Standard deviation0.5713014
Coefficient of variation (CV)0.293167
Kurtosis-0.43228403
Mean1.9487234
Median Absolute Deviation (MAD)0.32
Skewness0.71742329
Sum91.59
Variance0.32638529
MonotonicityNot monotonic
2023-11-28T13:23:03.765479image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
1.7 3
 
6.4%
1.8 3
 
6.4%
1.38 2
 
4.3%
2.85 2
 
4.3%
1.65 2
 
4.3%
1.48 2
 
4.3%
2 2
 
4.3%
2.7 1
 
2.1%
1.9 1
 
2.1%
1.93 1
 
2.1%
Other values (28) 28
59.6%
ValueCountFrequency (%)
0.98 1
2.1%
1.25 1
2.1%
1.28 1
2.1%
1.3 1
2.1%
1.38 2
4.3%
1.39 1
2.1%
1.4 1
2.1%
1.41 1
2.1%
1.48 2
4.3%
1.5 1
2.1%
ValueCountFrequency (%)
3.25 1
2.1%
3.18 1
2.1%
3 1
2.1%
2.86 1
2.1%
2.85 2
4.3%
2.83 1
2.1%
2.7 1
2.1%
2.64 1
2.1%
2.45 1
2.1%
2.41 1
2.1%

Flavanoids
Real number (ℝ)

HIGH CORRELATION 

Distinct41
Distinct (%)87.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3410638
Minimum0.34
Maximum3.17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size752.0 B
2023-11-28T13:23:03.952984image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.34
5-th percentile0.473
Q10.625
median0.92
Q31.94
95-th percentile3.03
Maximum3.17
Range2.83
Interquartile range (IQR)1.315

Descriptive statistics

Standard deviation0.90544064
Coefficient of variation (CV)0.67516596
Kurtosis-0.78262439
Mean1.3410638
Median Absolute Deviation (MAD)0.42
Skewness0.8527147
Sum63.03
Variance0.81982276
MonotonicityNot monotonic
2023-11-28T13:23:04.132017image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
3.03 2
 
4.3%
0.92 2
 
4.3%
0.6 2
 
4.3%
0.47 2
 
4.3%
0.76 2
 
4.3%
0.83 2
 
4.3%
0.7 1
 
2.1%
0.56 1
 
2.1%
0.66 1
 
2.1%
0.58 1
 
2.1%
Other values (31) 31
66.0%
ValueCountFrequency (%)
0.34 1
2.1%
0.47 2
4.3%
0.48 1
2.1%
0.49 1
2.1%
0.5 1
2.1%
0.52 1
2.1%
0.55 1
2.1%
0.56 1
2.1%
0.58 1
2.1%
0.6 2
4.3%
ValueCountFrequency (%)
3.17 1
2.1%
3.04 1
2.1%
3.03 2
4.3%
2.99 1
2.1%
2.68 1
2.1%
2.65 1
2.1%
2.63 1
2.1%
2.58 1
2.1%
2.55 1
2.1%
2.41 1
2.1%

Nonflavanoid_Phenols
Real number (ℝ)

HIGH CORRELATION 

Distinct26
Distinct (%)55.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.40255319
Minimum0.17
Maximum0.63
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size752.0 B
2023-11-28T13:23:04.307299image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.17
5-th percentile0.203
Q10.28
median0.43
Q30.5
95-th percentile0.607
Maximum0.63
Range0.46
Interquartile range (IQR)0.22

Descriptive statistics

Standard deviation0.12846025
Coefficient of variation (CV)0.31911372
Kurtosis-0.95356269
Mean0.40255319
Median Absolute Deviation (MAD)0.1
Skewness-0.15636798
Sum18.92
Variance0.016502035
MonotonicityNot monotonic
2023-11-28T13:23:04.474129image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0.43 7
14.9%
0.4 4
 
8.5%
0.53 3
 
6.4%
0.27 3
 
6.4%
0.5 3
 
6.4%
0.24 3
 
6.4%
0.45 2
 
4.3%
0.47 2
 
4.3%
0.17 2
 
4.3%
0.61 2
 
4.3%
Other values (16) 16
34.0%
ValueCountFrequency (%)
0.17 2
4.3%
0.2 1
 
2.1%
0.21 1
 
2.1%
0.22 1
 
2.1%
0.24 3
6.4%
0.25 1
 
2.1%
0.27 3
6.4%
0.29 1
 
2.1%
0.3 1
 
2.1%
0.32 1
 
2.1%
ValueCountFrequency (%)
0.63 1
 
2.1%
0.61 2
4.3%
0.6 1
 
2.1%
0.58 1
 
2.1%
0.56 1
 
2.1%
0.53 3
6.4%
0.52 1
 
2.1%
0.5 3
6.4%
0.48 1
 
2.1%
0.47 2
4.3%

Proanthocyanins
Real number (ℝ)

HIGH CORRELATION 

Distinct37
Distinct (%)78.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3644681
Minimum0.55
Maximum3.58
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size752.0 B
2023-11-28T13:23:04.662069image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.55
5-th percentile0.736
Q10.91
median1.25
Q31.64
95-th percentile2.624
Maximum3.58
Range3.03
Interquartile range (IQR)0.73

Descriptive statistics

Standard deviation0.61622557
Coefficient of variation (CV)0.45162329
Kurtosis3.1604942
Mean1.3644681
Median Absolute Deviation (MAD)0.37
Skewness1.5925393
Sum64.13
Variance0.37973395
MonotonicityNot monotonic
2023-11-28T13:23:04.838085image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
1.14 3
 
6.4%
1.35 3
 
6.4%
1.25 3
 
6.4%
1.87 2
 
4.3%
0.8 2
 
4.3%
1.66 2
 
4.3%
0.83 2
 
4.3%
1.46 1
 
2.1%
1.15 1
 
2.1%
0.94 1
 
2.1%
Other values (27) 27
57.4%
ValueCountFrequency (%)
0.55 1
2.1%
0.68 1
2.1%
0.73 1
2.1%
0.75 1
2.1%
0.8 2
4.3%
0.81 1
2.1%
0.83 2
4.3%
0.84 1
2.1%
0.86 1
2.1%
0.88 1
2.1%
ValueCountFrequency (%)
3.58 1
2.1%
2.91 1
2.1%
2.81 1
2.1%
2.19 1
2.1%
2.08 1
2.1%
1.98 1
2.1%
1.95 1
2.1%
1.87 2
4.3%
1.71 1
2.1%
1.66 2
4.3%

Color_Intensity
Real number (ℝ)

HIGH CORRELATION 

Distinct44
Distinct (%)93.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.9168085
Minimum1.28
Maximum10.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size752.0 B
2023-11-28T13:23:05.052624image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1.28
5-th percentile2.381
Q14.355
median5.24
Q37.93
95-th percentile10.442
Maximum10.8
Range9.52
Interquartile range (IQR)3.575

Descriptive statistics

Standard deviation2.5532631
Coefficient of variation (CV)0.43152709
Kurtosis-0.7844429
Mean5.9168085
Median Absolute Deviation (MAD)1.84
Skewness0.37739724
Sum278.09
Variance6.5191526
MonotonicityNot monotonic
2023-11-28T13:23:05.276346image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
5.1 2
 
4.3%
7.65 2
 
4.3%
4.9 2
 
4.3%
5.88 1
 
2.1%
8.42 1
 
2.1%
9.4 1
 
2.1%
8.6 1
 
2.1%
10.8 1
 
2.1%
10.52 1
 
2.1%
9.01 1
 
2.1%
Other values (34) 34
72.3%
ValueCountFrequency (%)
1.28 1
2.1%
2.08 1
2.1%
2.3 1
2.1%
2.57 1
2.1%
2.6 1
2.1%
2.8 1
2.1%
2.9 1
2.1%
3.4 1
2.1%
3.85 1
2.1%
4 1
2.1%
ValueCountFrequency (%)
10.8 1
2.1%
10.68 1
2.1%
10.52 1
2.1%
10.26 1
2.1%
10.2 1
2.1%
9.4 1
2.1%
9.2 1
2.1%
9.01 1
2.1%
8.6 1
2.1%
8.5 1
2.1%

Hue
Real number (ℝ)

HIGH CORRELATION 

Distinct34
Distinct (%)72.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.77361702
Minimum0.48
Maximum1.42
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size752.0 B
2023-11-28T13:23:05.513157image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.48
5-th percentile0.553
Q10.63
median0.75
Q30.89
95-th percentile1.037
Maximum1.42
Range0.94
Interquartile range (IQR)0.26

Descriptive statistics

Standard deviation0.18388826
Coefficient of variation (CV)0.23769935
Kurtosis2.1124349
Mean0.77361702
Median Absolute Deviation (MAD)0.14
Skewness1.0519761
Sum36.36
Variance0.033814894
MonotonicityNot monotonic
2023-11-28T13:23:05.803218image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
0.75 3
 
6.4%
0.89 3
 
6.4%
0.67 2
 
4.3%
0.78 2
 
4.3%
0.57 2
 
4.3%
0.59 2
 
4.3%
0.56 2
 
4.3%
0.7 2
 
4.3%
0.96 2
 
4.3%
0.87 2
 
4.3%
Other values (24) 25
53.2%
ValueCountFrequency (%)
0.48 1
2.1%
0.54 1
2.1%
0.55 1
2.1%
0.56 2
4.3%
0.57 2
4.3%
0.58 1
2.1%
0.59 2
4.3%
0.6 1
2.1%
0.61 1
2.1%
0.65 1
2.1%
ValueCountFrequency (%)
1.42 1
 
2.1%
1.19 1
 
2.1%
1.04 1
 
2.1%
1.03 1
 
2.1%
0.96 2
4.3%
0.93 1
 
2.1%
0.92 1
 
2.1%
0.91 2
4.3%
0.89 3
6.4%
0.87 2
4.3%

OD280
Real number (ℝ)

HIGH CORRELATION 

Distinct40
Distinct (%)85.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1508511
Minimum1.27
Maximum3.64
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size752.0 B
2023-11-28T13:23:06.078268image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1.27
5-th percentile1.309
Q11.59
median1.86
Q32.82
95-th percentile3.496
Maximum3.64
Range2.37
Interquartile range (IQR)1.23

Descriptive statistics

Standard deviation0.72792687
Coefficient of variation (CV)0.33843667
Kurtosis-0.82615047
Mean2.1508511
Median Absolute Deviation (MAD)0.3
Skewness0.74932768
Sum101.09
Variance0.52987752
MonotonicityNot monotonic
2023-11-28T13:23:06.406636image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
1.56 3
 
6.4%
1.75 3
 
6.4%
3 2
 
4.3%
1.82 2
 
4.3%
1.33 2
 
4.3%
3.36 1
 
2.1%
1.47 1
 
2.1%
2 1
 
2.1%
1.68 1
 
2.1%
1.86 1
 
2.1%
Other values (30) 30
63.8%
ValueCountFrequency (%)
1.27 1
 
2.1%
1.29 1
 
2.1%
1.3 1
 
2.1%
1.33 2
4.3%
1.42 1
 
2.1%
1.47 1
 
2.1%
1.51 1
 
2.1%
1.56 3
6.4%
1.58 1
 
2.1%
1.6 1
 
2.1%
ValueCountFrequency (%)
3.64 1
2.1%
3.53 1
2.1%
3.52 1
2.1%
3.44 1
2.1%
3.36 1
2.1%
3.33 1
2.1%
3.13 1
2.1%
3.12 1
2.1%
3.05 1
2.1%
3 2
4.3%

Proline
Real number (ℝ)

Distinct39
Distinct (%)83.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean631.95745
Minimum365
Maximum1080
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size752.0 B
2023-11-28T13:23:06.745265image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum365
5-th percentile387.8
Q1520
median590
Q3707.5
95-th percentile988.5
Maximum1080
Range715
Interquartile range (IQR)187.5

Descriptive statistics

Standard deviation172.62386
Coefficient of variation (CV)0.27315742
Kurtosis0.59322894
Mean631.95745
Median Absolute Deviation (MAD)90
Skewness0.86813603
Sum29702
Variance29798.998
MonotonicityNot monotonic
2023-11-28T13:23:07.084581image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
520 3
 
6.4%
675 2
 
4.3%
480 2
 
4.3%
600 2
 
4.3%
580 2
 
4.3%
550 2
 
4.3%
680 2
 
4.3%
630 1
 
2.1%
685 1
 
2.1%
695 1
 
2.1%
Other values (29) 29
61.7%
ValueCountFrequency (%)
365 1
2.1%
372 1
2.1%
380 1
2.1%
406 1
2.1%
415 1
2.1%
463 1
2.1%
480 2
4.3%
500 1
2.1%
510 1
2.1%
515 1
2.1%
ValueCountFrequency (%)
1080 1
2.1%
1065 1
2.1%
1035 1
2.1%
880 1
2.1%
855 1
2.1%
845 1
2.1%
835 1
2.1%
830 1
2.1%
770 1
2.1%
760 1
2.1%

kmeans_labels
Categorical

CONSTANT 

Distinct1
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size752.0 B
1
47 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters47
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 47
100.0%

Length

2023-11-28T13:23:07.350781image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-28T13:23:07.572709image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 47
100.0%

Most occurring characters

ValueCountFrequency (%)
1 47
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 47
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 47
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 47
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 47
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 47
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 47
100.0%

Interactions

2023-11-28T13:22:59.058903image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:32.633275image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:34.584288image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:36.441830image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:38.348255image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:41.568552image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:43.741548image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:45.651025image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:47.561756image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:49.486576image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:51.463368image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:54.038375image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:56.406395image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:59.223689image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:32.806349image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:34.731216image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:36.589809image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:38.494715image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:41.856801image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:43.895047image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:45.800764image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:47.718590image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:49.643696image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:51.613116image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:54.342923image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:56.568693image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:59.371126image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:32.946844image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:34.866705image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:36.738894image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:38.640286image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:42.118792image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:44.035683image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:45.933417image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:47.853430image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:49.795372image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:51.753136image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:54.602460image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:56.703131image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:59.546010image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:33.092161image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:35.007560image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:36.895468image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:38.784999image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:42.296169image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:44.195749image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:46.077809image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:48.007790image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:49.943743image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:51.886407image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:54.849831image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:56.840676image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:59.708809image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:33.244653image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:35.148337image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:37.049135image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:38.943639image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:42.453078image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:44.346417image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:46.247397image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:48.167447image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:50.107732image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:52.046334image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:55.083905image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:56.987821image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:59.863158image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:33.389165image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:35.291600image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:37.203761image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:39.100171image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:42.590882image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:44.511308image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:46.387490image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:48.324775image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:50.251358image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:52.184468image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:55.257134image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:57.904307image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:00.008485image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:33.525920image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:35.426424image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:37.342158image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:39.244299image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:42.736714image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:44.645721image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:46.522677image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:48.463730image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:50.413968image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:52.405010image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:55.395464image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:58.050859image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:00.158302image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:33.679876image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:35.567509image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:37.491715image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:39.392704image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:42.883060image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:44.797850image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:46.664504image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:48.619013image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:50.557384image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:52.619709image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:55.539180image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:58.201560image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:00.311450image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:33.843474image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:35.705291image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:37.632007image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:40.264961image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:43.037869image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:44.933467image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:46.805053image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:48.753181image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:50.713085image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:52.838157image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:55.679781image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:58.337238image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:00.474012image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:34.003651image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:35.872719image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:37.788641image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:40.463433image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:43.196128image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:45.080875image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:46.959532image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:48.903881image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:50.860651image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:53.105497image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:55.831368image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:58.503312image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:00.635236image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:34.147256image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:36.011697image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:37.932647image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:40.741095image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:43.326700image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:45.224093image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:47.101324image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:49.044063image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:51.007167image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:53.341589image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:55.969059image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:58.631195image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:00.785826image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:34.288613image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:36.159030image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:38.071260image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:40.960762image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:43.459559image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:45.354909image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:47.262125image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:49.189902image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:51.144900image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:53.534022image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:56.107241image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:58.765040image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:00.923565image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:34.431766image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:36.294600image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:38.203652image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:41.244385image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:43.596725image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:45.495677image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:47.399616image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:49.331435image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:51.304117image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:53.803357image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:56.248302image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:58.907920image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-11-28T13:23:07.722167image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
AlcoholAshAsh_AlcanityColor_IntensityFlavanoidsHueMagnesiumMalic_AcidNonflavanoid_PhenolsOD280ProanthocyaninsProlineTotal_Phenols
Alcohol1.0000.154-0.0750.347-0.0600.0620.2490.1670.038-0.075-0.0210.2710.022
Ash0.1541.0000.5720.200-0.0710.0810.311-0.0830.111-0.009-0.1580.0260.092
Ash_Alcanity-0.0750.5721.0000.184-0.328-0.187-0.0120.0710.351-0.349-0.214-0.282-0.180
Color_Intensity0.3470.2000.1841.000-0.316-0.6240.358-0.1120.114-0.618-0.2040.206-0.384
Flavanoids-0.060-0.071-0.328-0.3161.0000.3910.2210.071-0.6180.5600.7630.0090.714
Hue0.0620.081-0.187-0.6240.3911.0000.022-0.057-0.0830.6990.2850.1140.524
Magnesium0.2490.311-0.0120.3580.2210.0221.000-0.193-0.443-0.0770.1250.3990.036
Malic_Acid0.167-0.0830.071-0.1120.071-0.057-0.1931.000-0.0180.0540.188-0.0160.131
Nonflavanoid_Phenols0.0380.1110.3510.114-0.618-0.083-0.443-0.0181.000-0.217-0.305-0.114-0.189
OD280-0.075-0.009-0.349-0.6180.5600.699-0.0770.054-0.2171.0000.4660.0850.701
Proanthocyanins-0.021-0.158-0.214-0.2040.7630.2850.1250.188-0.3050.4661.0000.0830.677
Proline0.2710.026-0.2820.2060.0090.1140.399-0.016-0.1140.0850.0831.0000.165
Total_Phenols0.0220.092-0.180-0.3840.7140.5240.0360.131-0.1890.7010.6770.1651.000

Missing values

2023-11-28T13:23:01.176667image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-11-28T13:23:01.506689image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

AlcoholMalic_AcidAshAsh_AlcanityMagnesiumTotal_PhenolsFlavanoidsNonflavanoid_PhenolsProanthocyaninsColor_IntensityHueOD280Prolinekmeans_labels
1913.643.102.5615.21162.703.030.171.665.100.963.368451
2112.933.802.6518.61022.412.410.251.984.501.033.527701
3914.223.992.5113.21283.003.040.202.085.100.893.537601
4113.413.842.1218.8902.452.680.271.484.280.913.0010351
4313.243.982.2917.51032.642.630.321.664.360.823.006801
4514.214.042.4418.91112.852.650.301.255.240.873.3310801
4614.383.592.2816.01023.253.170.272.194.901.043.4410651
7912.703.872.4023.01012.832.550.431.952.571.193.134631
8313.053.862.3222.5851.651.590.611.624.800.842.015151
9912.293.172.2118.0882.852.990.452.812.301.422.834061
AlcoholMalic_AcidAshAsh_AlcanityMagnesiumTotal_PhenolsFlavanoidsNonflavanoid_PhenolsProanthocyaninsColor_IntensityHueOD280Prolinekmeans_labels
16212.853.272.5822.01061.650.600.600.965.580.872.115701
16312.963.452.3518.51061.390.700.400.945.280.681.756751
16513.734.362.2622.5881.280.470.521.156.620.781.755201
16613.453.702.6023.01111.700.920.431.4610.680.851.566951
16712.823.372.3019.5881.480.660.400.9710.260.721.756851
16913.404.602.8625.01121.980.960.271.118.500.671.926301
17012.203.032.3219.0961.250.490.400.735.500.661.835101
17413.403.912.4823.01021.800.750.431.417.300.701.567501
17513.274.282.2620.01201.590.690.431.3510.200.591.568351
17714.134.102.7424.5962.050.760.561.359.200.611.605601